用印尼情绪词汇分析印尼人的情绪,并考虑否认

F. Saputra, Yani Nurhadryani
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引用次数: 3

摘要

公众舆论的数量可能非常反对一件时髦的事情。看到这一点,不可能手动阅读每个意见,从所有意见中得出结论,因为这将花费相当多的时间。情感分析可以用来解决这个问题。然而,民意并不总是只用一句话来写,它甚至可以用不止一句话来写。还应该注意的是,印尼语的句子根据其形式分为两种,即简单句和复合句。简单句只由一个分句组成,而复合句由一个或多个分句组成。复合句中的每个分句根据是否存在否定分句而有差异。有鉴于此,本文试图通过考虑印尼语的否定、句式以及作为停顿语调或结束语调的标点符号来提高表现。本文中使用的数据是在2018年西爪哇选举期间从Twitter获得的。模型构建方法包括数据预处理、特征提取、分类和评价4个阶段。该模型的准确率从60.15%提高到63.7%,提高了3%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of Indonesian Sentiments Using Indonesian Sentiment Lexicon by Considering Denial
The amount of public opinion can be very much against a trendy thing. Seeing this, it is not possible to read every opinion manually to get a conclusion from all opinions because it will take quite a lot of time. Sentiment analysis can be used as a solution to the problem. However, public opinion is not always written in just one sentence, it can even be written in more than one sentence. It should also be noted that sentences in Indonesian are divided into two based on their forms, namely simple sentences and compound sentences. Simple sentences only consist of one clause, while compound sentences consist of one or more clauses. Each clause in compound sentences can have differences based on whether there is denial the clause or not. Seeing this, this paper seeks to improve performance by considering the denial, sentence form in Indonesian, and punctuation that is used as a paused or finished intonation. Data used in this paper obtained from Twitter during the campaign of the election in West Java 2018. The methodology of model building consist of 4 stages: data preprocessing, feature extraction, classification, and evaluation. The model resulted in this paper has improved accuracy by 3% from 60.15% to 63.7%.
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